RESEARCH ARTICLE Open Access Mycobacterium tuberculosis...Tuberculosis (TB) is the leading infectious cause of death world-over. TB is caused by infection with the tu-bercle bacillus

RESEARCH ARTICLE Open Access

Mycobacterium tuberculosis thymidylatekinase antigen assays for designatingincipient, high-risk latent M.tb infectionMisaki Wayengera1,2* , David P. Kateete2,3, Benon Asiimwe3 and Moses L. Joloba2,3

Abstract

Background: Precise designation of high risk forms of latent Mycobacterium tuberculosis-M.tb infections (LTBI) isimpossible. Delineation of high-risk LTBI can, however, allow for chemoprophylaxis and curtail majority cases ofactive tuberculosis (ATB). There is epidemiological evidence to support the view that LTBI in context of HIV-1co-infection is high-risk for progression to ATB relative to LTBI among HIV-ve persons. We recently showed thatassays of M.tb thymidylate kinase (TMKmt) antigen and host specific IgG can differentiate ATB from LTBI and or noTB (NTB, or healthy controls). In this study, we aimed to expose the differential levels of TMKmt Ag among HIV+veco-infected LTBI relative to HIV-ve LTBI as a strategy to advance these assays for designating incipient LTBI.

Methods: TMKmt host specific IgM and IgG detection Enzyme Immuno-Assays (EIA) were conducted on 40 TBexposed house-hold contacts (22 LTBI vs. 18 no TB (NTB) by QunatiFERON-TB GOLD®); and TMKmt Ag detection EIAdone on 82 LTBI (46 HIV+ve vs 36 HIV-ve) and 9 NTB (American donors). Purified recombinant TMKmt protein wasused as positive control for the Ag assays.

Results: IgM levels were found to be equally low across QuantiFERON-TB GOLD® prequalified NTB and TB exposedhouse-hold contacts. Higher TMKmt host specific IgG trends were found among TB house-hold contacts relative toNTB controls. TMKmt Ag levels among HIV+ve LTBI were 0.2676 ± 0.0197 (95% CI: 0.2279 to 0.3073) relative to 0.1069 ±0.01628 (95% CI: 0.07385 to 0.14) for HIV-ve LTBI (supporting incipient nature of LTBI in context of HIV-1 co-infection).NTB had TMKmt Ag levels of 0.1013 ± 0.02505 (5% CI: 0.0421 to 0.1606) (intimating that some were indeed LTBI).

Conclusions: TMKmt Ag levels represent a novel surrogate biomarker for high-risk LTBI, while host-specific IgG can beused to designate NTB from LTBI.

Keywords: Tuberculosis, Mycobacterium tuberculois, Latent M.tb infections (LTBI);Thymidylate Kinase, Serodiagnosis

BackgroundTuberculosis (TB) is the leading infectious cause ofdeath world-over. TB is caused by infection with the tu-bercle bacillus Mycobacterium tuberculosis (M. tb) [1, 2].An estimated 1/3 of the global human population isthought to be infected with M.tb. Majority (90–95%) ofthese infections remain asymptomatic (a.k.a latent M.tb

infection, LTBI) [3–5]. Only 10-5% progress to active TB(ATB) disease. In 2016, there were an estimated 1.3million TB deaths among HIV-negative people (downfrom 1.7 million in 2000) and an additional 374 000deaths among HIV-positive people. About 10.4 millionpeople fell ill with TB in 2016: 90% were adults, 65%were male, 10% were people living with HIV (74% inAfrica) and 56% were in five countries: India, Indonesia,China, the Philippines and Pakistan [3–5]. Hopes oftotally controlling TB have been dampened because ofthe (i) difficulty of developing an effective vaccine, (ii)expensive and time-consuming diagnostic process, (iii)necessity of many months of treatment, (iv) increase in

* Correspondence: [email protected] of Pathology, Unit of Genetics & Genomics, School ofBiomedical Sciences, Makerere University College of Health Sciences, P o Box7072, Kampala, Uganda2Department of Immunology &Molecular Biology, School of BiomedicalSciences, Makerere University College of Health Sciences, P o Box 7072,Kampala, UgandaFull list of author information is available at the end of the article

© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Wayengera et al. BMC Infectious Diseases (2018) 18:133 https://doi.org/10.1186/s12879-018-3007-y

HIV-TB co-infections, and (iv) emergence of drug-resistant cases in the 1980s [3–5].Because LTBI forms the vast reservoir from which

ATB accrues, it has been proposed that identifying thoseincipient forms of LTBI that are at high-risk of progres-sing to ATB and treating or offering them chemo-prophylaxis, can drastically reduce global TB incidence[3, 5]. Accurate designation of high-risk or incipientLTBI is, however, currently impossible. Specifically, thetwo available methods (the tuberculin skin test-TSTand interferon gamma release assays- IGRA) for testingfor LTBI cannot designate high-risk LTBI. TST if posi-tive provides evidence of M.tb infection. That said, manyHIV infected patients will have a negative skin test des-pite M.tb infection or disease, due to anergy. “Two stageor booster test” is not a substitute to anergy testing.However, the same might have some utility in detectingM.tb infection in anergic HIV-TB co-infected patients[6]. TST underestimates the prevalence of LTBI in en-demic countries, requires trained health care staff to cor-rectly perform and accurately read the results, and alsodemands a second patient visit [7–13]. The test is nei-ther useful to rule in disease nor in high TB prevalencesettings to identify eligible individuals for prophylaxis.Interferon-γ release assay (IGRA) is used to diagnoseLTBI and is particularly useful in profoundly ill patientsand those with severe malnutrition. Two in vitro IGRAtests are available: QuantiFERON- TB Gold (Cellestis,USA) and the T SPOT-TB test (Oxford Immunotec,USA). Both use an enzyme- linked immunospot assay toquantify the number of peripheral blood mononuclearcells (PBMCs) producing IFN- γ in response to TB-specific antigen stimulation (ESAT-6 and CFP10). Bothassays have sensitivity (as measured in patients withATB) comparable to that of the TST, but are signifi-cantly more expensive [14]. IFN-γ assays do not differ-entiate between LTBI and ATB or between immunereconstitution inflammatory syndrome (IRIS) and failure.Studies suggest that IGRAs are ideal for serial testing be-cause these can be repeated without boosting [15–17].These are also unaffected by previous BCG vaccinationand require fewer patient visits. However, WHO recom-mended against the use of IGRAs for diagnosis of activeor latent TB, in resource-limited settings [18, 19].There is epidemiologically evidenced high risk for

acquiring ATB (over 20% per year) among HIV-1 co-infected persons [20]. This has been used to argue the in-cipient nature of LTBI among HIV+ve people and is theglobally acceptable rationale underlying recommendationsfor isoniazid-INH chemo-prophylaxis. Overall, TB is themost common opportunistic infection (OI) among HIV-infected individuals, and co-infected individuals are athigh risk of death [21–23]. TB may occur at any stage ofHIV disease and is frequently the first recognized

presentation of underlying HIV infection [24, 25]. Ascompared to people without HIV, people living with HIV(PLWH) have a 20-fold higher risk of developing TB [26,27] and the risk continues to increase as CD4 cell countsprogressively decline [24, 26, 27]. Although antiretroviraltherapy (ART) can reduce the incidence of TB both at theindividual and population level, PLWH on ART still havehigher TB incidence rates and a higher risk of dyingfrom TB [28, 29]. However, co-administration of ARTalong with anti-TB therapy presents several manage-ment challenges, including drug-drug interactions, over-lapping drug toxicities and immune reconstitutionsyndrome. This emphasizes the importance of routine TBscreening among PLWHA to not only identify those with-out TB, but if possible, prevent TB by chemoprophylaxisas well as to diagnose and promptly treat TB. In absenceof accurate tests for high-risk LTBI, WHO recommendsINH chemo-prophylaxis for all PLWHA [30–32].Our group recently demonstrated the capability of

TMKmt Ag and Ab assays to differentiate between ATBIand LTBI or NTB [33]. In the current study, we—ridingon the evidence that LTBI among HIV-1 co-infected per-sons is high-risk, set out to examine LTBI diagnostic po-tential of TMKmt assays. Specifically, in light of the abovedescribed existing epidemiological data pointing to thehigh-risk of LTBI among HIV-1 co-infected persons toprogress to ATB relative to their HIV-1 negative counter-parts, it was argued that a point cross-sectional study ofTMKmt Ag levels among HIV-ve LTBI relative to HIV-veLTBI participants, can inform the diagnostic potential ofTMKmt Ag assays for high risk LTBI. As a secondaryoutcome, we also examined the ability of TMKmt hostspecific IgM and IgG to differentiate between NTB andLTBI relative to the QuantiFERON-TB GOLD® assay.

MethodsStatement of ethics and consent to participateThis protocol was approved by the School of BiomedicalSciences Institutional Review & Ethics Committee (SBS-IREC) at the College of Health Sciences, MakerereUniversity Kampala, Uganda as protocol # SBS 263 titled“Exploration of Mycobacterium tuberculosis thymidylatekinase based culture- and immunodiagnostic- assays to-wards rapid and easy detection of Tuberculosis”. Sincethe study used broadly consented serum of TB house-hold contacts and LTBI previously collected by theMakerere University-Case-Western Reserve University(MU-CWRU) TB Research Unit Project, the need forparticipant consent was waived by the SBS-IREC.

TMKmt Peptide-epitopes and polyclonal antibodies(PAbs)The identity of the two TMKmt epitopes used to derivepolyclonal antibodies (PAb-0655 and PAb-0656) used for

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TMKmt antigen (Ag) detection Enzyme immuno-assays(EIAs) in this study, was derived as follows: First, (a)using the entire 214 amino acids sequences of TMKmt(SP “|O05891|” ) and five best performing biophysicalprofiles (accessibility, antigenicity, beta-turn, flexibility,and hydophilicity) in the immune epitope database ana-lysis resource (IEDB-AR), the 27 AA long 148_ERSRGRAQRDPGRARDNYERDAELQQR peptide was predictedto be the best continuous linear B cell epitope by all pro-files (4/5, 80%) except antigenicity (see Fig. 1). Second,(b) using the crystal structure of TMKmt (PdB entry:“1g3u”) and discontinuous B cell epitope software Disco-Tope, we derived 22 amino acids ( A:G57, A:E148, A:S150, A:R151, A:G152, A:R153, A:A154, A:Q155, A:R156,A:D157, A:P158, A:G159, A:A160, A:A161, A:R162, A:A163, A:N164, A:E166, A:R167, A:D168, A:A169, A:T179)as the best discontinuous or non-linear epitope (see Fig. 2).These two epitopes are designated UG-Peptide 1 and 2, re-spectively. The referenced two synthetic TMKmt-peptide-epitopes and their derivative polyclonal antibodies (PAbs,GeneCUST, Luxemburg) were used to detected TMKmt

host specific serum antibody (Ab, IgG) and TMKmt Anti-gen (Ag) levels by direct enzyme immune-assays (EIA) aspreviously described [33].

MethodsDesignCross-Sectional Laboratory Study

SiteImmunology laboratory, Dept of Immunology and Mo-lecular Biology, School of Biomedical Sciences, College ofHealth Sciences, Makerere University Kampala, Uganda.

Samples and participants40 TB exposed house-hold contacts (pre-qualified byQunatiFERON-TB GOLD® as 22 LTBI vs. 18 no TB(NTB); 82 LTBI (46 HIV+ve vs 36 HIV-ve) and 9 NTB.

Materials and reagentsTMKmt peptide epitopes 1 and 2 (denoted UG-peptide 1and 2 respectively, GeneCUST, Luxemburg), New Zealand

Fig. 1 Biophysical Profiles of the TMKmt B cell Epitopes in the Immune Epitope Database Analysis Resource and DiscoTope. This figure shows the5 biophysical profiles of the linear B cell epitope (UG-Peptide 1) within the IEDB-AR (Plates 1 to 5: surface accessibility, antigenicity, beta-turn,flexibility and hydrophilicity) alongside the DiscoTope profile of the non-linear B cell epitope (UG-Peptide 1)

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Rabbit derived anti-UG-peptide 1 polyclonal antibody(Pab-0655); plain ELISA plates (flat bottom, Nunc), BovineSerum Albumin (BSA, In-vitrogen, USA), goat anti humanIgM and IgG (HRP labeled, Bio-Rad, France), PhosphateBuffered Saline (PBS), and the enzymatic substrate tetra-methylbenzidine (TMB).Interventions (a) TMKmt host specific IgM or IgG

antibodies: For detection of host TMKmt specific IgMand IgG humoral responses in serum of 40 TB exposedhouseholds, we (i) dissolved 1ug (conc: 10mg/ml) of in-dividual synthetic peptide by adding 100 μl of freshlyprepared phosphate buffered saline (PBS was preparedby dissolving ¼ of a 250mg tablet in 50ml PCR gradewater). (ii) 100 μl (0.001ng) of individual syntheticpeptide (UG-Peptide-01 & UG-Peptide-02) was thenpipetted into each of the wells of a sterile 96-well micro-titer plate (Nunc) and the plate incubated overnight. (iii)The plated wells were then blocked once the followingday using 5% BSA in PBS and incubated at 37°C for 30mins, after which excess solution was discarded andplate left to dry. (iv) 100μls of PBS was added to eachassigned wells, followed by addition of 10μl (1:100 dilu-tion) of samples into the respective wells; after whichthe plate was shaken at 15HZ for 16 secs, and incubatedfor 1 hour at 37°C. Blank wells were also made, by add-ing only PBS rather than sample. The wells-in-use werethen washed with PBS three times using an automatedplate-washer. (v) 100μls of either goat anti-human IgMor IgG horse-raddish peroxidate conjugate was added,

and the plates incubated at 37°C for another 1 hour.During this incubation, the enzyme substrate was pre-pared by adding 1 volume of substrate (TMB) to 1 vol-ume of diluent (hydrogen peroxide) in volumes enoughfor all the wells in use. (vi) 200μl of freshly preparedsubstrate was added to each well (purple-bluish colordeveloped in all except A-BX1 blank wells). (vii) The re-action was stopped by adding 100μl of dilute (1mol/L)H2SO4. The intensity of the reaction in each well washence after determined by reading the plate at an opticaldensity (OD) of 450nm using a single filter of an auto-mated ELISA plate reader (PR 3100, Bio-Rad). (b)TMKmt Ag EIA. For detection of TMKmt Ag amongserum of the 82 LTBI categories (i) dissolved 1uL ofserum was dissolved in 1000 μl or 1 ml of freshly pre-pared phosphate buffered saline. (ii) 100 μl of resultantserum-diluent was then pipetted into each of the wellsof a sterile 96-well microtiter plate (Nunc) and the plateincubated overnight. (iii) The plated wells were thenblocked once the following day using 5% BSA in PBSand incubated at 37°C for 30 mins, after which excesssolution was discarded and plate left to dry. Blank wellswere also made, by adding only PBS rather than sample.The wells-in-use were then washed with PBS three timesusing an automated plate-washer. (iv) antipeptide-1rabbit derived polyclonal antibody (Pab-0655, IgG dom-inant) was added and plates incubated at 37°C for 30mins, after which excess solution was discarded andplate left to dry. The wells-in-use were then washed withPBS three times using an automated plate-washer. (v)100μls of goat anti-rabbit IgG horse-raddish peroxidateconjugate was added, and the plates incubated at 37°Cfor another 1 hour. During this incubation, the enzymesubstrate was prepared by adding 1 volume of substrate(TMB) to 1 volume of diluent (hydrogen peroxide) involumes enough for all the wells in use. (vi) 200μl offreshly prepared substrate was added to each well (purple-bluish color developed in all except A-BX1 blank wells).(vii) The reaction was stopped by adding 100μl of dilute(1mol/L) H2SO4. The intensity of the reaction in eachwell was hence after determined by reading the plate at anoptical density (OD) of 450nm using a single filter of anautomated ELISA plate reader (PR 3100, Bio-Rad).

Measured variablesLevels of host specific IgM and IgG antibodies andTMKmt Ag levels in study serum or blanks was qualita-tively detected as a function of the OD of each well.

Treatment of resultsRaw data was cleaned by subtracting ODs of blanksfrom those of test wells. The issuing adjusted ODs wereeither run as duplicates in GraphPad® (IgM and IgG) oraveraged across the duplicate runs for each test (Ag).

Fig. 2 Loci of the non-linear B cell epitope on the 3-D Crystal structure ofTMKmt. This figure shows the loci of the 22 amino acids ( A:G57, A:E148,A:S150, A:R151, A:G152, A:R153, A:A154, A:Q155, A:R156, A:D157, A:P158,A:G159, A:A160, A:A161, A:R162, A:A163, A:N164, A:E166, A:R167, A:D168,A:A169, A:T179) on the 3-D crystal structure of TMKmt (PdB entry: “1g3u”).

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Resultant average adjusted ODs were analyzed by bothPRISM® software, and Excel® . Graphs were also drawnby GraphPad®. For each OD read (essentially done in du-plicate), a 95% Confidence interval (CI) read was com-puted, alongside the slopes and P-values. Excel sheetswere used for correction of average sample OD readingsby subtracting OD reading of the blank wells. Only CIswere considered as this exploratory study aimed to dem-onstrate the accuracy and reliability (replicability) ofthese assays. Reference to sensitivity, specificity, positiveand negative predictive values (PPV and NP as well asreceiver operator characteristics (ROCs) curves is madefrom previous data. To strengthen replicability, testingfor Ag capture was done in separate categories of LTBIand results analyzed and presented separate clusters ofthe same group.

ResultsIgM and IgG levels among NTB and TB exposed house-hold contactsIgM levels among NTB and TB exposed house-hold contactsTMKmt host specific IgM levels captured by UG-Peptide 1 and UG-Peptide 2 among NTB were 0.02490+0.001599 (95% CI: 0.02151 to 0.02829) and 0.02713+0.0006373 (95% CI: 0.02577 to 0.02848) respectively (seeTable 1). This when compared to IgM levels captured bythe same respective peptide epitopes among TB exposedhouse-hold contacts of 0.02623+0.0009368 (95% CI:0.02436 to 0.02809) and 0.02704+0.0006148 (95% CI:

0.02581 to 0.02826) (see Table 2). Overall, these datashow that TMKmt host specific IgM levels are similarlylow (<0.03) among both NTB and TB exposed house-hold contacts (see Fig. 3). Moreover, there were no ob-served differences in the capability of either of the twoepitopes to capture TMKmt host specific IgM. There-fore, TMKmt host specific IgM levels can not differenti-ate NTB from LTBI. This is expected, as IgM levels arelikely to only be high within 1 to 3 months following theinitial exposure to M.tb after which they wane and arereplaced by IgG levels. It should therefore not besurprising that similarly low IgM levels were foundamong TB exposed participants. The relevance ofTMKmt host specific IgM levels—would thereby be re-stricted to evaluating risk of exposure to M.tb amongnatives of low TB endemic areas who recently travelledto high TB endemic settings. Indeed, the presence ofslightly high levels of IgM among pockets of our pur-ported NTB controls as pre-qualified Quantiferon® Goldassays, might suggest that these persons had inter-currently or recently been exposed to M.tb. The lattermight actually be a representation of the limitation ofQuantiFERON TB Gold ® for differentiating TB exposurefrom NTB [6–19]. For details, see Additional file 1.

IgG levels among NTB and TB exposed house-hold contactsUnlike the case above, detected IgG levels among NTBcontrols were in general significantly below those seenamong TB exposed house-hold contacts. Specifically,

Table 1 Showing statistics of TMKmt host specific IgM captureamong 9 NTB (duplicates)

Host Specific IgM

UG-Peptide 1 UG-Peptide 2

Best-fit values

• YIntercept 0.02490 0.02713

• Slope 7.500e-005 -0.000125

Std. Error

• YIntercept 0.001599 0.0006373

• Slope 0.0002841 0.0001132

95% Confidence Intervals

• YIntercept 0.02151 to 0.02829 0.02577 to 0.02848

• Slope -0.0005272 to0.0006772

-0.0003651 to0.0001151

Goodness of Fit

• Degrees of Freedom 16 16

• R square 0.004338 0.07075

• Absolute Sum of Squares 0.0001549 2.463e-005

• Sy.x 0.003112 0.001241

Number of points

• Analyzed 18 18

Table 2 Showing statistics of TMKmt host specific IgM captureamong 40 exposed TB house-hold contacts (duplicates)

Host Specific IgM

UG-Peptide 1 UG-Peptide 2

Best-fit values

• YIntercept 0.02623 0.02704

• Slope -0.0001115 -0.0001045

Std. Error

• YIntercept 0.0009368 0.0006148

• Slope 3.905e-005 2.551e-005

95% Confidence Intervals

• YIntercept 0.02436 to 0.02809 0.02581 to 0.02826

• Slope -0.0001892 to-3.375e-005

-0.0001553 to-5.377e-005

Goodness of Fit

• Degrees of Freedom 79 80

• R square 0.09352 0.1735

• Absolute Sum of Squares 0.001368 0.0005975

• Sy.x 0.004161 0.002733

Number of points

• Analyzed 80 80

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IgG levels captured by UG-peptide 1 and 2 were 0.5155+0.07675 (95% CI: 0.3528 to 0.6782) and 0.3277+0.04226 (95% CI: 0.2381 to 0.4173) respectively amongNTB controls (see Table 3) compared to 1.020+0.1183(95% CI: 0.7854 to 1.256) and 1.209 +0.1209 (95% CI:0.9689 to 1.449) among TB household contacts withLTBI (see Table 4). These trends imply that TMKmt hostspecific IgG levels are a good potential biomarker fordifferentiating NTB from LTBI. This data is consistentwith the immune-pathogenesis of TB, wherein produc-tion of host specific IgG antibody responses to M.tbfollowing initial exposure is delayed, but persist onceestablished (see Fig. 4). This data also supports the viewthat although the designated NTB controls used in thisstudy might not be appropriate by virtue of their

measured high IgG levels (due to impreciseness of existingmethods for differentiating LTBI from NTB; which issimilarly inferred by slightly raised IgM levels amongsome NTB pointing to perhaps a more recent exposure toM.tb), they actually at least offer extremities that are con-sistent with TB biology. As we shall discuss further, someof the host specific IgG antibody levels seen among theTB exposed house-hold contacts are way above the upperdesignated limit for LTBI (QuantiFERON TB-Gold®) andqualify to be designated ATB [ATB=OD>1.00; 95%CI1.170 to 1.528 ] as per our prior work [33]. Overall, ourprior work supports the view that levels of IgG amongtrue NTB must be lower [=OD<0.088] compared to LTBI[=0.255>OD<1.00]. Indeed, a subsequent attempt tocategorize these 40 TB household contacts as NTB andLTBI by the QuantiFERON TB Gold® assay and TMKmt-IgG assay exposes similarly high TMKmt host specific IgGlevels among both clusters (see Fig. 5). For details, seeAdditional file 1.

TMKmt Ag levels among HIV+ve and HIV-ve LTBIcategories versus healthy controlsIn order to investigate the above contradictory IgG re-sults in context of the impreciseness of the existing tech-nologies for differentiating between LTBI and NTB, wefocused our next evaluations on TMKmt Ag levels. Ourearlier work shows that TMKmt Ag levels captured byPab-655 (derived from UG-peptide 1) can preciselydesignate ATBI from either LTBI and or NTB [33]. Tovalidate the above findings—we undertook assays ofTMKmt Ag levels among an expanded cohort of 82LTBI (46 HIV+ve vs 36 HIV-ve) and 9 NTB controls. Inline with the prevailing knowledge base that LTBIamong HIV-1 co-infected is high risk [20–30]; we showthat TMKmt Ag levels among HIV+ve LTBI are abovethose of HIV-ve LTBI. Precisely, it is confirmed thatHIV+ve LTBI presents with higher TMKmt Ag levels(>0.14: 0.2676 ± 0.0197 [95% CI: 0.2279 to 0.3073]) rela-tive to HIV-ve LTBI (<0.14: 0.1069 ± 0.01628[95% CI:0.07385 to 0.14]) (see Table 5). Indeed, as was the case for

Fig. 3 TMK host specific IgM levels among the NTB controls and TB exposed house-hold contacts. This figure shows TMKmt host specific IgMlevels among the 9 NTB controls (American donors) and 40 TB exposed house-hold contacts. IgM levels were found to be within the same rangeacross both categories. This is consistent with TB epidemiology and immune-pathogenesis wherein exposure occurs early and is followed by M.tbhost-specific IgM responses that last only about 1 to 3 months; being replaced by IgG antibodies

Table 3 Showing Statistics of TMKmt host specific IgG Captureamong 9 NTB (duplicates)

Host Specific IgG

UG-Peptide 1 UG-Peptide 2

Best-fit values

• YIntercept 0.5155 0.3277

• Slope -0.02662 -0.002550

Std. Error

• YIntercept 0.07675 0.04226

• Slope 0.01364 0.007510

95% Confidence Intervals

• YIntercept 0.3528 to 0.6782 0.2381 to 0.4173

• Slope -0.05553 to 0.002295 -0.01847 to 0.01337

Goodness of Fit

• Degrees of Freedom 16 16

• R square 0.1923 0.007154

• Absolute Sum of Squares 0.3571 0.1083

• Sy.x 0.1494 0.08227

Number of points

• Analyzed 18 18

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TMKmt host specific IgG levels, some QuantiFERON-TBGold® assay pre-qualified NTB had TMKmt Ag levels thatlay within ranges of HIV-ve LTBI or epidemiologically lowrisk LTBI (0.1013 ± 0.02505 [95% CI: 0.0421 to 0.1606])(see Figs. 6, 7 and 8). Two incidental HIV-ve LTBI hadhigh TMKmt Ag levels, possibly due to false negativeHIV-1 test, or another form of immune-deficiency. Fordetails, see Additional file 1.

DiscussionWe present data to support the view that TMKmt Aglevel are a potentially more precise and accurate bio-marker for incipient LTBI relative to existing assays.Brust B, et al. (2011) recently intimated that thosepathogen antigens whose secretory levels depend on the

physiology of M.tb represent the best candidates for re-search and development of TB immune-diagnostics [34].Our group has previously shown that TMKmt Ag levelsrepresent a predictive (foretelling) surrogate biomarkerfor both in-vitro and in-vivo growth and proliferationof M.tb [33, 35, 36]. Growth and proliferation is aphysiologic change that assails M.tb exit from dormancy[37–39]. Arguably, there should be more active growthand proliferation of M.tb among high-risk LTBI relative tolow risk LTBI. Here, using a conceivably novel design ofcross-sectional study, we present validation that TMKmtAg levels are objectively a surrogate biomarker for high-risk LTBI.First, we show similarly low levels of TMKmt host spe-

cific IgM (ODs <0.03) captured by EIAs premised onour custom peptide epitopes (see Figs. 1 and 2 respect-ively) among both NTB and TB exposed householdcontacts (see Fig. 3 and Tables 1 and 2). For details, seeAdditional file 1. Exposure to M.tb within high TB en-demic areas likely occurs immediately following birth[1–5]. As a consequence, IgM levels are bound to onlybe high within 1 to 3 months following the initial expos-ure to M.tb after which they wane and are replaced byIgG levels. Assays of levels of TMKmt host specific IgMare therefore not usable for differentiating between NTBand LTBI, but might be relevant towards evaluating TBexposure among natives of low TB endemic areas (ob-jectively NTB) who travel to TB high endemic areas andreturn home within 1-3 months.Second, we show that levels of TMKmt host specific

IgG can clearly differentiate NTB from LTBI. Specific-ally, IgG levels were 0.5155+0.07675 (95% CI: 0.3528 to0.6782) and 0.3277+ 0.04226 (95% CI: 0.2381 to 0.4173)among NTB controls (see Table 3) compared to 1.020+0.1183 (95% CI: 0.7854 to 1.256) and 1.209 +0.1209(95% CI: 0.9689 to 1.449) (see Table 4) among LTBI (allcaptured by the TMKmt epitope peptides UG-peptide 1and 2 based EIAs, respectively (see Fig. 4). This data isconsistent with our prior work that found that levels ofIgG among NTB are =OD<0.88 (95% CI: 0.1527 to

Table 4 Showing statistics of TMKmt host specific IgG captureamong 40 TB exposed house-hold contacts (duplicates)

Host Specific IgG

UG-Peptide 1 UG-Peptide 2

Best-fit values

• YIntercept 1.020 1.209

• Slope 0.005842 0.003592

Std. Error

• YIntercept 0.1183 0.1209

• Slope 0.004384 0.004481

95% Confidence Intervals

• YIntercept 0.7854 to 1.256 0.9689 to 1.449

• Slope -0.002867 to0.01455

-0.005309 to0.01249

Goodness of Fit

• Degrees of Freedom 80 80

• R square 0.01935 0.007091

• Absolute Sum of Squares 28.04 29.30

• Sy.x 0.5582 0.5706

Number of points

• Analyzed 80 80

Fig. 4 TMKmt host specific IgG levels among the NTB controls and TB exposed house-hold contacts. This figure shows TMKmt host specific IgGlevels among the 9 NTB controls (American donors) and 40 TB exposed house-hold contacts. Higher IgG levels were observed among the TB ex-posed house-hold contacts relative to the NTB controls

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0.8751) compared to LTBI=0.255>OD<1.00 (95% CI:0.2690 to 0.6396) [33]. Indeed, a subsequent attempt tore-categorize the 40 TB exposed household contacts asNTB and LTBI by the QuantiFERON TB Gold® assayalongside the TMKmt-IgG assay revealed high IgGlevels among both clusters (see Fig. 5); suggesting thatQuantiFERON TB Gold® might be inaccurate towardsdifferentiating LTBI from NTB [3–19]. For details, see

Additional file 1. Interestingly, our early work alsoshowed higher TMKmt host specific IgG among HIV+ve persons relative to the HIV-ve persons, a findingwe attributed to the myco-septicemia that assails thedisseminated nature of TB disease among HIV-1 co-infected persons [33]. These data support the high-risknature of LTBI among HIV+ve co-infected persons. Itis on basis of these and existing epidemiological data that

Fig. 5 Discordance of distribution of TMKmt host specific IgG levels among the TB house-hold contacts. This figure shows the discordanceobserved in the distribution of TMKmt host specific IgG levels among the TB house-hold contacts after categorization by QuantiFERON TB Gold®Assay as 18 NTB and 22 LTBI. Considering TMKmt host specific IgG levels, some of the NTB are actually LTBI and vice versa

Table 5 TMKmt Ag levels among 82 LTBI categorized as HIV+ve (46), HIV-ve (36) and NTB 9

HIV+, LTBI HIV-, LTBI NTB rTMKmt Pos. Control

Best-fit values ± SE Perfect line

• Slope -0.001101 ± 0.0007299 0.0007494 ± 0.0007674 -0.003667 ± 0.004451 0.991

• Y-intercept 0.2676 ± 0.0197 0.1069 ± 0.01628 0.1013 ± 0.02505 1.5045

• X-intercept 243 -142.7 27.64 -0.01816

• 1/slope -908 1334 -272.7 1.009

95% Confidence Intervals

• Slope -0.002572 to 0.0003697 -0.0008101 to 0.002309 -0.01419 to 0.006859

• Y-intercept 0.2279 to 0.3073 0.07385 to 0.14 0.0421 to 0.1606 1.009 to 2.000

• X-intercept 117 to +infinity -infinity to -33.12 10.42 to +infinity

• Goodness of Fit

• R square 0.04919 0.02729 0.08837

• Sy.x 0.06572 0.04783 0.03448

Is slope significantly non-zero?

• F 2.276 0.9537 0.6786

• DFn, DFd 1, 44 1, 34 1, 7

• P value 0.1385 0.3357 0.4372

• Deviation from zero? Not Significant Not Significant Not Significant

Equation Y = -0.001101∗X + 0.2676 Y = 0.0007494∗X + 0.1069 Y = -0.003667∗X + 0.1013 Y = 0.991∗X + 0.018

Data

• Number of X values 46 36 9 2

• Maximum number of Y replicates 1 1 1 1

• Total number of values 46 36 9 2

• Number of missing values 0 0 0 0

Wayengera et al. BMC Infectious Diseases (2018) 18:133 Page 8 of 13

we moved to examine the ability of TMKmt Ag detectionas a surrogate for incipient LTBI (assuming that all LTBIamong PLWHA is high-risk)[20–30, 33].In line with the primary hypothesis of this study (that

LTBI among HIV+ve persons is high risk)—we showthat TMKmt Ag levels among HIV+ve LTBI are abovethose of HIV-ve LTBI. Specifically, we show that HIV+veLTBI presents with higher TMKmt Ag levels (>0.14:

0.2676 ± 0.0197 [95% CI: 0.2279 to 0.3073]) relative toHIV-ve LTBI (<0.14: 0.1069 ± 0.01628[95% CI: 0.07385to 0.14]) (see Table 5). These data support the prevailingWHO & STOP TB Partnership recommendations forisoniazid (INH) prophylaxis among PLWHA within highTB endemic areas [25–30]. As was the case for TMKmthost specific IgG levels, we similarly noted that someQuantiFERON-TB Gold® assay pre-qualified NTB controls

0 10 20 30 40 50

0.0

0.5

1.0

1.5

2.0

2.5

TMKmt Ag Levels Among 46 HIV+ LTBI, 36HIV-ve LTB and 9NTB

Serial serum samples

cOD

s

HIV+, LTBI

HIV-, LTBI

NTB

rTMKmt Pos. Control

Fig. 6 TMKmt Ag levels among HIV+ve LTBI and HIV-ve LTBI relative to NTB controls. This figure shows TMKmt Ag levels among 46 HIV+ve LTBIand 36 HIV-ve LTBI relative to 9 NTB controls. Recombinant TMKmt Ag cloned and expressed in E.coli BL21 (DE) was used as a positive control.TMKmt Ag levels among HIV+ve controls where higher than those among HIV-ve LTBI, supporting the high-risk nature of LTBI among PLWHA.Two incidental HIV-ve LTBI had high TMKmt Ag levels, possibly due to false negative HIV-1 test, or another form of immune-deficiency

0.000

0.500

1.000

1.500

2.000

2.500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 101

103

105

ODs

Serum samples: LTBI+, LTBI-, 2 incidental LTBI+, NTB, 2 rTMKmt + controls

Fig. 7 Bar-Graph of TMKmt Ag levels among HIV+ve LTBI and HIV-ve LTBI relative to NTB controls. This figure shows a Bar-Graph of the sameTMKmt Ag levels among 46 HIV+ve LTBI and 36 HIV-ve LTBI relative to 9 NTB controls shown in Fig. 6

Wayengera et al. BMC Infectious Diseases (2018) 18:133 Page 9 of 13

had TMKmt Ag levels that lay within ranges of HIV-veLTBI (0.1013 ± 0.02505 [95% CI: 0.0421 to 0.1606]) (seeFigs. 6, 7 and 8). For details, see Additional file 1. Two in-cidental HIV-ve LTBI had high TMKmt Ag levels, possiblydue to false negative HIV-1 test, or another form ofphysiological or biological immune-deficiency.In order to offer a hint on the performance of TMKmt

Ag and host specific IgG levels for the designation of TBstatus, we a re-evaluated in-house data from our priorstudy data for receiver operator characteristics [33]. Onone hand: (a) for n=128 and prevalence of 80.0 (95% CI:73.0,86.9), the sensitivity, specificity, PPV and NPV ofpre-set TMKmt Ag capture-EIA-OD cut-off for differen-tiating ATB from NTB alone at 95% CI were respect-ively: 99.0 (94.7,100.0), 68.0 (46.5,85.1), 92.7 (86.2,96.8),and 94.4 (72.7,99.9) [yielding a ROC-area of 83.5 (95%CI: 74.1, 92.9)] compared to 96.6 (91.4, 99.1), 21.1 (9.5,37.3), 78.9 (71.2,85.3) and 66.7 (34.9,90.1) [ROC-area58.8 (52.0,65.6)] obtained using pre-set TMKmt host-specific IgG-EIA-OD cut-offs on n=154 participants andprevalence of 74.0 (68.0, 81.9). On the other hand, (b)for n=220 and prevalence of 63.0 (95% CI: 56.0, 69.1),the sensitivity, specificity, PPV and NPV of pre-setTMKmt Ag capture-EIA-OD cut-off for differentiatingATB from both LTBI & NTB at 95% CI were respectively:73.9 (65.8,81.0), 90.2 (81.7,95.7), 92.7 (86.2,96.8) and 67.3(57.7,75.9) [ ROC-area of 82.1 (77.2,87.0)] compared to92.6 (86.3,96.5), 34.8 (21.4,50.2), 78.9 (71.2,85.3) and 64.0(42.5,82.0) [ROC-area 63.7 (56.3,71.0)] obtained usingpre-set TMKmt host-specific IgG-EIA-OD cut-offs forn=167 participants and prevalence of 72.0 (65.0,79.1) (fora summary, see Tables 6, 7 and 8). These sensitivity resultsare above the documented values for smear microscopy

for AFBs (~45%) and close or above those of GenXpert®(72.5 %) [19]. The observed high prevalence rates are as aresult of using already pre-qualified specimen rather thanundertaking a prospective recruitment and testing.A key limitation of our work—as is the case for all

projects that aim to advance novel biomarkers for LTBI,is the absence of a gold standard for precisely designat-ing LTBI and NTB [3–19]. As noted, we maneuveredaround this challenge using epidemiological data aroundLTBI in context of HIV-1 co-infection; and the same de-sign might be relevant to global TB diagnostic biomarkerstudies [4, 6–14, 40–44, 45–49]. However, the best ap-proach would be a prospective follow-up cohort to re-cruit high-risk LTBI among HIV+ve persons thateventually develop TB. Second, our sample size issmall and a larger validation study is needed. Third,much as host immunological and transcriptomic pro-files have been studied in an attempt to expose markers ofTB disease progression, our group focused on a markerfor growth and proliferation of the pathogen (M.tb) as asurrogate for disease progression. Although several M.tbtargets inclusive of lip-arabinomannose (LAM), IP-10,early secretory antigen 6(ESAT-6) and colony filtrate pro-tein 10 (CFP-10) have previously been developed for

Serum samples: LTBI+, LTBI-, 2 incidental LTBI+, NTB, 2 rTMKmt + controls

0.000

0.500

1.000

1.500

2.000

2.500

0 20 40 60 80 100 120

ODs

Serum samples: LTBI+, LTBI-, 2 incidental LTBI+,NTB, 2rTMKmt+ controls

Fig. 8 Dot-Graph of TMKmt Ag levels among HIV+ve LTBI and HIV-ve LTBI relative to NTB controls. This figure shows a Dot-Graph of the sameTMKmt Ag levels among 46 HIV+ve LTBI and 36 HIV-ve LTBI relative to 9 NTB controls shown in Fig. 6

Table 6 Cut-offs values associated with TB status: UG-Peptide 1based TMKmt host specific Antibody (IgG) capture EIAs (HIV+ve)

TB status EIA values 95% CI

Active TB (ATB) ATB=OD>1.00 1.170 to 1.528

Latent M.tb Infection (LTBI) LTBI=0.255>OD<1.00 0.2690 to 0.6396

No TB (NTB) NTB=OD<0.88 0.1527 to 0.8751

Wayengera et al. BMC Infectious Diseases (2018) 18:133 Page 10 of 13

the purpose of detecting TB, none meets the criteriafor designating delineating high-risk LTBI [3–7]. Onpart of the host, however, recent data report that se-quential inflammatory processes and a whole bloodRNA signature might possess TB disease progressiondetection capability that are better than the aforelisted M.tb targets [50, 51]. It might therefore beworthwhile to adopt assays of TMKmt Ag levels as anew pathogen target for expanded clinical testing to-wards accurately designating incipient TB. Last butmore important to note is that INF- responses—which are a correlate of memory, might not presentthe best biomarker for differentiating between highand low-risk LTBI especially within high TB endemicareas [3–19].

ConclusionIn conclusion, TMKmt Ag and host specific IgG anti-bodies offer us novel surrogate biomarkers for LTBI

in-context of HIV-1 co-infection. Precisely, Levels ofTMKmt Ag represent a novel surrogate biomarker forhigh-risk LTBI, while host-specific IgG can be used to des-ignate NTB from LTBI. TMKmt host specific IgM levelsmight be relevant towards evaluating TB exposure amongresidents of low TB endemic areas (NTB) who recentlytravelled to a high TB endemic area.

Additional file

Additional file 1: This file shows processing of the duplicate EIA readingsfor TMKmt Ag capture among the 46 HIV+ve LTBI and 36 HIV-ve LTBI relativeto the 9 NTB controls and 2 rTMKmt positive controls. Overall, readings ofblanks were subtracted from the average ODs of the test wells to obtain acorrected average ODs used to draw the graphs. (XLSX 29 kb)

AbbreviationsAb: Antibody; Ag: Antigen; OD: Optical Density; EIA: Enzyme Immuno-Assays;M.tb: Mycobacterium tuberculosis; TB: Tuberculosis; TMKmt: Mycobacteriumtuberculosis thymidylate kinase; LTBI: Latent M.tb Infections; ATB: Active TB;NTB: No TB

AcknowledgementsWe thank Mr. Ronald Ssenyonga who did the secondary analyses forsensitivity, specificity, PPV, NPV and ROCs. Ms. Geraldine Nalwadde and Ms.Joanitta Basemera (Dept of Medical Microbiology, MakCHS) alongside Ms.Harriet Nambooze (MEPI-MESAU Office, MakCHS) for their administrativeassistance. Staff of the Immunology and the Mycobacteriology Level III atMaKCHS offered us invaluable technical support. Also, Mr. Nestar B Mugabaand Ms. Christine Zaake at the Sida/SAREC Office (Directorate of Researchand Graduate Training, Makerere University) offered us administrativeassistance. Dr. Irene Andia provided samples for the TB exposed house-holdcontacts. Investigators for the Makerere University-Case Western ReserveUniversity TB Research Unit (TBRU) provided broadly consented participantserum samples for LTBI. Dr. Benson Ouma provided the gift of 9 serumsamples of TB naive American donors used as negative controls.

FundingThis work was done as a partial fulfillment for the award of the degree ofDoctor of Philosophy to MW. This research was funded in part by GrantNumber 5R24TW008886 OGAC, NIH and HRSA [MEPI/MESAU PI: NelsonSewankambo], and the International Research on TB and other Pulmonarycomplications of AIDS NIH Forgarty award Number D43-TW009607-01[PI:John Lucian Davies and Art Reingold] ‘Pulmonary complications of AIDSresearch training (PART)’. ‘This work was also funded in part by the SwedishInternational Development Corporation Agency (Sida) and MakerereUniversity’. The content of this manuscript is solely the responsibility of theauthor(s) and does not necessarily represent the official views of thesupporting offices.

Availability of data and materialsAll data and materials are available as described in the methodologies,references and supporting files.

Authors’ contributionsThere are 4 authors to this manuscript: MW, DPK, BA, and MLJ. MWconceived the concept behind this work. MW and MLJ supplied materialsand reagents. MW, DPK, BA and MLJ did the experiments and analyzed thedata. MW wrote the final draft of the Manuscript. All authors read andapproved the final version of the manuscript.

Ethics approval and consent to participateThis protocol was approved by the School of Biomedical SciencesInstitutional Review & Ethics Committee (SBS-IREC) at the College ofHealth Sciences, Makerere University Kampala, Uganda as protocol # SBS263 titled “Exploration of Mycobacterium tuberculosis thymidylate kinasebased culture- and immunodiagnostic- assays towards rapid and easy

Table 7 Cut-offs values for TB status: PAb-655 based TMKmtAntigen capture EIAs (HIV+ve)

Sample TB Status EIA values 95% CI

Active TB (ATB) ATB=OD>0.490 0.7446 to 0.8715

Latent M.tb Infection (LTBI) LTBI=0.26>OD<0.490 0.4325 to 0.4829

No TB (NTB) NTB=OD<0.26 0.1675 to 0.2567

Table 8 Receiver operator characterization of TMKmt Ag andhost specific IgG EIA-ODs for detecting TB exposure and ordisease status among HIV positive individuals

Among HIVpositive individuals

Active TB vs OD values classification

% (95% CI)

ATB vs No TB ATB vs (LTBI & No TB)

TMKmt Ag Capture

n 128 220

Prevalence 80.0 (73.0,86.9) 63.0 (56.0,69.1)

Sensitivity 99.0 (94.7,100.0) 73.9 (65.8,81.0)

Specificity 68.0 (46.5,85.1) 90.2 (81.7,95.7)

PPV 92.7 (86.2,96.8) 92.7 (86.2,96.8)

NPV 94.4 (72.7,99.9) 67.3 (57.7,75.9)

ROC area 83.5 (74.1,92.9) 82.1 (77.2,87.0)

TMKmt Ab Capture

n 154 167

Prevalence 74.0 (68.0, 81.9) 72.0 (65.0,79.1)

Sensitivity 96.6 (91.4, 99.1) 92.6 (86.3,96.5)

Specificity 21.1 (9.5, 37.3) 34.8 (21.4,50.2)

PPV 78.9 (71.2,85.3) 78.9 (71.2,85.3)

NPV 66.7 (34.9,90.1) 64.0 (42.5,82.0)

ROC 58.8 (52.0,65.6) 63.7 (56.3,71.0)

Wayengera et al. BMC Infectious Diseases (2018) 18:133 Page 11 of 13

detection of Tuberculosis”. Since the study used broadly consentedserum of TB house-hold contacts and LTBI previously collected by theMakerere University-Case-Western Reserve University (MU-CWRU) TB Re-search Unit Project, the need for participant consent was waived by theSBS-IREC.

Consent for publicationNot Applicable

Competing interests“Tehe authors declare that they have no competing interests.” M.W haspreviously filed a related patent # UG/P/2013/000006 at the UgandaRegistration Services Bureau (URBS) and African Regional Intellectaul PropertyOrganization (ARIPO).

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.

Author details1Department of Pathology, Unit of Genetics & Genomics, School ofBiomedical Sciences, Makerere University College of Health Sciences, P o Box7072, Kampala, Uganda. 2Department of Immunology &Molecular Biology,School of Biomedical Sciences, Makerere University College of HealthSciences, P o Box 7072, Kampala, Uganda. 3Department of MedicalMicrobiology, School of Biomedical Sciences, Makerere University College ofHealth Sciences, P o Box 7072, Kampala, Uganda.

Received: 30 November 2017 Accepted: 21 February 2018

References1. DEJ S, Raviglione M, Kochi A. In: Blooe BR, editor. Pathogenesis, Protection,

and Control. Tuberculosis. Washington DC; 1996. Am. Soc. Microbiol.2. Wheeler PR & Ratledgee C. Pathogenesis, Protection, and Control.

Tuberculosis, ed. B.R. Blooem. 1994, Washington DC: Am. Soc. Microbiol.3. Weldingh K, Andersen P. ESAT-6/CFP10 Skin Test Predicts Disease in M.

tuberculosis-Infected Guinea Pigs. PLoS ONE. 2008;3(4):e1978.4. Whalen CC. Diagnosis of latent tuberculosis infection: measure for measure.

JAMA. 2005;293:2785–7.5. WHO. Preventive therapy against tuberculosis in people living with HIV.

Wkly Epidemiol Rec. 1999;74:385–98.6. Wanchu A. Advances in serology for diagnosing TB in the HIV infected.

Indian J Chet Dis Allied Sci. 2005;47:31–7.7. Morris K. WHO recommends against inaccurate tuberculosis tests. Lancet.

2011;377:113–4.8. Dowdy DW, Steingart KR, Pai M. Serological testing versus other strategies

for diagnosis of active tuberculosis in India: a cost-effectiveness analysis.PLoS Med. 2011;8:e1001074.

9. Khosla R, et al. Peripheral blood based C-PCR assay for diagnosing extra-pulmonary tuberculosis. Indian J Exp Biol. 2009;47:447–53.

10. Kashyap RS, Ramteke SS, Morey SH, Purohit HJ, Taori GM, Daginawala HF.Diagnostic value of early secreted antigenic target-6 for the diagnosis oftuberculous meningitis patients. Infection. 2009;37:508–13.

11. Mutetwa R, Boehme C, Dimairo M, Bandason T, Munyati SS, Mangwanya D,et al. Diagnostic accuracy of commercial urinary lipoarabinomannandetection in African tuberculosis suspects and patients. Int J Tuberc LungDis. 2009;13:1253–9.

12. Shah M, Variava E, Holmes CB, Coppin A, Golub JE, McCallum J, et al.Diagnostic accuracy of a urine lipoarabinomannan test for tuberculosis inhospitalized patients in a high HIV prevalence setting. J Acquir ImmuneDefic Syndr. 2009;52:145–51.

13. Swaminathan S, Subbaraman R, Venkatesan P, Subramanyam S, Kumar SR,Mayer KH, et al. Tuberculin skin test results in HIV-infected patients in India:Implications for latent tuberculosis treatment. Int J Tuberc Lung Dis. 2008;12:168–73.

14. Pai M, Riley LW, Colford JM Jr. Interferon-gamma assays in theimmunodiagnosis of tuberculosis: a systematic review. Lancet Infect Dis.2004;4:761–76.

15. Liebeschuets S, Bamber S, Ewer K, Deeks J, Pathan AA, Lalvani A. Diagnosisof tuberculosis in South African children with T-cell based assay: Aprospective cohort study. Lancet. 2004;364:2196–203.

16. Rangaka MX, Wilkinson KA, Seldon R, Van Cutsem G, Meintjes GA, MorroniC, et al. The effect of HIV-1 infection on T-cell based and skin test detectionof tuberculosis infection. Am J Respir Crit Care Med. 2007;175:514–20.

17. Pai M. Alternatives to the tuberculin skin test: interferon γ assays in thediagnosis of Mycobacterium tuberculosis infection. Indian J Med Micro.2005;23:151–8.

18. WHO (2010). Strategic and Technical Advisory Group (STAG-TB) Report ofthe 10th Meeting. 2010. [Accessed 30 Sept 2015]. Available from: http://www.who.int/tb/advisory_bodies/stag_tb_report_2010.pdf.

19. Padmapriyadarsini C, Narendran G, Swaminathan S. Diagnosis & treatmentof tuberculosis in HIV co-infected patients. Indian J Med Res. 2011;134(6):850–65.

20. Global Report. UNAIDS repot on the global AIDS epidemic 2010. [Accessed20 Sept 2011]. Available at the following url: http://www.unaids.org/globalreport/documents/20101123_GlobalReport_full_en.pdf.

21. Corbett E, Watt CJ, Walker N, Maher D, Williams BG, Raviglione MC, et al.The growing burden of tuberculosis: global trends and interactions withthe HIV epidemic. Arch Intern Med. 2003;163:1009–21.

22. Lawn S, Churchyard G. Epidemiology of HIV associated tuberculosis. CurrOpin HIV AIDS. 2009;4:325–33.

23. WHO (2010). Global tuberculosis control. [Accessed 20 Sept 2011]. Availableat the following url: http://whqlibdoc.who.int/publications/2010/9789241564069_eng.pdf.

24. Sonnenberg P, Glynn JR, Fielding K, Murray J, Godfrey-Faussett P, Shearer S.How soon after infection with HIV does the risk of tuberculosis start toincrease? A retrospective cohort study in South African gold miners. J InfectDis. 2007;191:150–8.

25. Havlir DV, Getahun H, Sanne I, Nunn P. Opportunities and challenges forHIV care in overlapping HIV and TB epidemics. JAMA. 2008;300:423–4230.

26. Getahun H, Gunneberg C, Granich R, Nunn P. HIV infection-associatedtuberculosis: the epidemiology and the response. Clin Infect Dis. 2010;50:S201–7.

27. WHO (2007). Towards universal access by 2010: How WHO is working withcountries to scale-up HIV prevention, treatment, care and support. Availableat the following url: https://www.who.int/hiv/mediacentre/universal_access_progress_report_en.pdf.

28. Lawn SD, Myer L, Bekker LG, Wood R. Burden of tuberculosis in anantiretroviral treatment programme in sub-Saharan Africa: impact ontreatment outcomes and implications for tuberculosis control. AIDS. 2006;20:1605–12.

29. Nunn P, Williams B, Floyd K, Dye C, Elzinga G, Raviglione M. Tuberculosiscontrol in the era of HIV. Nat Rev Immunol. 2005;5:819–26.

30. WHO (2004). Interim policy on collaborative TB/HIV activities. (Report no:WHO/HTM/TB/ 2004.330)

31. Day JH, Charalambous S, Fielding KL, Hayes RJ, Churchyard GJ, GrantAD. Screening for tuberculosis prior to isoniazid preventive therapyamong HIV-infected gold miners in South Africa. Int J Tuberc Lung Dis.2006;10:523–9.

32. Mohammed A, Ehrlich R, Wood R, Cilliers F, Maartens G. Screening fortuberculosis in adults with advanced HIV infection prior to preventivetherapy. Int J Tuberc Lung Dis. 2004;8:792–5.

33. Wayengera M, et al. Sero-diagnosis of Active Mycobacterium tuberculosisDisease among HIV Co-infected Persons using Thymidylate Kinase basedAntigen and Antibody Capture Enzyme Immuno-Assays. Mycobact Dis.2017;7(2)

34. Brust B, et al. Mycobacterium tuberculosis Lipolytic Enzymes as PotentialBiomarkers for the Diagnosis of Active Tuberculosis. PLoS ONE. 2011;6(9):e25078.

35. Wayengera M, Mwebaza I, Welishe J, Bayiyana A,5, Kateete DP,Wampande E, et al. Immuno-diagnosis of Mycobacterium tuberculosisin sputum, and reduction of timelines for its positive cultures to within3 h by pathogen-specific thymidylate kinase expression assays. BMC ResNotes 2017; 10(1):368.

36. Wayengera M. Theoretical Basis for Reducing Time-lines to the Designationof Positive Mycobacterium tuberculosis cultures using Thymidylate kinase(TMK) Assays. BMC Theor Biol Med Model. 2009;6:4.

37. Zahrt T. Molecular mechanisms regulating persistent Mycobacteriumtuberculosis infection. Microbes and Infection. 2003;5(2):159–67.

Wayengera et al. BMC Infectious Diseases (2018) 18:133 Page 12 of 13

38. Moors MA, PDA. Identification of bacterial genes that contribute to survivaland growth in an intracellular environment. Trends Microbiol. 1995;3:83–5.

39. Douglous Y, Stark J, Kirschner K. Systems biology of persistent infection:tuberculosis as a case study. Nat Rev Microbiol. 2008;6:520–8.

40. Steingart KR, Henry M, Laal S, Hopewell PC, Ramsay A, Menzies D, et al.Commercial serological antibody detection tests for the diagnosis ofpulmonary tuberculosis: a systematic review. PLoS Med. 2007;4(6):e202.

41. Chaudahary M, Gupta S, Khare S, Lal S. Diagnosis of tuberculosis in an eraof HIV pandemic: A review of current status and future prospects. Indian JMed Micro. 2010;28:281–9.

42. Morgan M, Kalantri S, Flores L, Pai M. Commercial line probe assay for therapid detection of rifampicin resistance in mycobacterium tuberculosis:A systematic review and meta analysis. BMC Infect Dis. 2005;5:62.

43. Gennaro ML. Immunologic diagnosis of tuberculosis. Clin Infect Dis. 2000;30:S243–6.

44. Chan ED, Heifets L, Iseman MD. Immunologic diagnosis of tuberculosis:A review. Tuberc Lung Dis. 2000;80:131–40.

45. Pai M, Ramsay A, O’Brien R. Evidence-Based Tuberculosis Diagnosis. PloSONE. 2008;5(7)

46. Flores LL, Pai M, Colford JM Jr, Riley LW. In-house nucleic acid amplificationtests for the detection of Mycobacterium tuberculosis in sputum specimens:meta-analysis and Meta-regression. BMC Microbiol. 2005;5(55)

47. Okamoto M, Kawabe T, Iwasaki Y, Hara T, Hashimoto N, Imaizumi K, et al.Evaluation of interferon-gamma, interferon-gamma-inducing cytokines, andinterferon-gamma-inducible chemokines in tuberculous pleural effusions.J Lab Clin Med. 2005;145:88–93.

48. Pokkali S, DRLS. Expression of CXC and CC type of chemokines and itsreceptors in tuberculous and non-tuberculous effusions. Cytokine. 2008:307–14.

49. Boehme C, Molokova E, Minja F, Geis S, Loscher T, Maboko L, et al.Detection of mycobacterial lipoarabinomannan with an antigen-captureELISA in unprocessed urine of Tanzanian patients with suspectedtuberculosis. Trans R Soc Trop Med Hyg. 2005;99:893–900.

50. Scriba TJ, Penn-Nicholson A, Shankar S, Hraha T, Thompson EG, Sterling D,et al. Sequential inflammatory processes define human progression from M.tuberculosis infection to tuberculosis disease. PLoS Pathog. 2017;13(11):e1006687.

51. Zak DE, Penn-Nicholson A, Scriba TJ, Thompson E, Suliman S, Amon LM,et al. A blood RNA signature for tuberculosis disease risk: a prospectivecohort study. Lancet. 2016;387(10035):2312–22.

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